Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms

• URL: http://arxiv.org/abs/2310.15330v3
• Date: Fri, 14 Jun 2024 23:03:32 GMT
• Title: Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms
• Authors: Ye Tian, Haolei Weng, Yang Feng,
• Abstract summary: We introduce a federated gradient EM algorithm (FedGrEM) designed for the unsupervised learning of mixture models. We present a comprehensive finite-sample theory that holds for general mixture models. We then apply this general theory on specific statistical models to characterize the explicit estimation error of model parameters and mixture proportions.
• Score: 13.857921574409362
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While supervised federated learning approaches have enjoyed significant success, the domain of unsupervised federated learning remains relatively underexplored. Several federated EM algorithms have gained popularity in practice, however, their theoretical foundations are often lacking. In this paper, we first introduce a federated gradient EM algorithm (FedGrEM) designed for the unsupervised learning of mixture models, which supplements the existing federated EM algorithms by considering task heterogeneity and potential adversarial attacks. We present a comprehensive finite-sample theory that holds for general mixture models, then apply this general theory on specific statistical models to characterize the explicit estimation error of model parameters and mixture proportions. Our theory elucidates when and how FedGrEM outperforms local single-task learning with insights extending to existing federated EM algorithms. This bridges the gap between their practical success and theoretical understanding. Our numerical results validate our theory, and demonstrate FedGrEM's superiority over existing unsupervised federated learning benchmarks.

Related papers

• Uncertainty quantification for Markov chains with application to temporal difference learning [63.49764856675643]
  We develop novel high-dimensional concentration inequalities and Berry-Esseen bounds for vector- and matrix-valued functions of Markov chains. We analyze the TD learning algorithm, a widely used method for policy evaluation in reinforcement learning.
  arXiv  Detail & Related papers  (2025-02-19T15:33:55Z)
• Network EM Algorithm for Gaussian Mixture Model in Decentralized Federated Learning [1.4549461207028445]
  We study various network Expectation-Maximization (EM) algorithms for the Gaussian mixture model. We introduce a momentum network EM (MNEM) algorithm, which uses a momentum parameter to combine information from both the current and historical estimators. We also develop a semi-supervised MNEM algorithm, which leverages partially labeled data.
  arXiv  Detail & Related papers  (2024-11-08T14:25:46Z)
• Multi-Agent Reinforcement Learning from Human Feedback: Data Coverage and Algorithmic Techniques [65.55451717632317]
  We study Multi-Agent Reinforcement Learning from Human Feedback (MARLHF), exploring both theoretical foundations and empirical validations. We define the task as identifying Nash equilibrium from a preference-only offline dataset in general-sum games. Our findings underscore the multifaceted approach required for MARLHF, paving the way for effective preference-based multi-agent systems.
  arXiv  Detail & Related papers  (2024-09-01T13:14:41Z)
• Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction [22.567754688492414]
  Cross-device Federated Learning (FL) faces significant challenges where low-end clients that could potentially make unique contributions are excluded from training large models due to their resource bottlenecks. Recent research efforts have focused on model-heterogeneous FL, by extracting reduced-size models from the global model and applying them to local clients accordingly. This paper presents a unifying framework for heterogeneous FL algorithms with online model extraction and provides a general convergence analysis for the first time.
  arXiv  Detail & Related papers  (2023-10-12T19:07:58Z)
• Deep Equilibrium Models Meet Federated Learning [71.57324258813675]
  This study explores the problem of Federated Learning (FL) by utilizing the Deep Equilibrium (DEQ) models instead of conventional deep learning networks. We claim that incorporating DEQ models into the federated learning framework naturally addresses several open problems in FL. To the best of our knowledge, this study is the first to establish a connection between DEQ models and federated learning.
  arXiv  Detail & Related papers  (2023-05-29T22:51:40Z)
• Federated Compositional Deep AUC Maximization [58.25078060952361]
  We develop a novel federated learning method for imbalanced data by directly optimizing the area under curve (AUC) score. To the best of our knowledge, this is the first work to achieve such favorable theoretical results.
  arXiv  Detail & Related papers  (2023-04-20T05:49:41Z)
• Proof of Swarm Based Ensemble Learning for Federated Learning Applications [3.2536767864585663]
  In federated learning it is not feasible to apply centralised ensemble learning directly due to privacy concerns. Most distributed consensus algorithms, such as Byzantine fault tolerance (BFT), do not normally perform well in such applications. We propose PoSw, a novel distributed consensus algorithm for ensemble learning in a federated setting.
  arXiv  Detail & Related papers  (2022-12-28T13:53:34Z)
• Adversarial Robustness with Semi-Infinite Constrained Learning [177.42714838799924]
  Deep learning to inputs perturbations has raised serious questions about its use in safety-critical domains. We propose a hybrid Langevin Monte Carlo training approach to mitigate this issue. We show that our approach can mitigate the trade-off between state-of-the-art performance and robust robustness.
  arXiv  Detail & Related papers  (2021-10-29T13:30:42Z)
• Fine-tuning is Fine in Federated Learning [3.222802562733787]
  We study the performance of federated learning algorithms and their variants in an framework. This multi-criterion approach naturally models the high-dimensional, many-tuned nature of federated learning.
  arXiv  Detail & Related papers  (2021-08-16T18:59:24Z)
• Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimization [69.07420650261649]
  We introduce a novel, simple, and powerful contrastive MI estimator named as FLO. Empirically, our FLO estimator overcomes the limitations of its predecessors and learns more efficiently. The utility of FLO is verified using an extensive set of benchmarks, which also reveals the trade-offs in practical MI estimation.
  arXiv  Detail & Related papers  (2021-07-02T15:20:41Z)
• Federated Multi-armed Bandits with Personalization [19.85013388155711]
  We propose a new bandit paradigm analogous to the federated learning (FL) framework in supervised learning. Under the PF-MAB framework, a mixed bandit learning problem that flexibly balances generalization and personalization is studied. We then propose the Personalized Federated Upper Confidence Bound (PF-UCB) algorithm, where the exploration length is chosen carefully.
  arXiv  Detail & Related papers  (2021-02-25T18:59:43Z)
• FedDANE: A Federated Newton-Type Method [49.9423212899788]
  Federated learning aims to jointly learn low statistical models over massively distributed datasets. We propose FedDANE, an optimization that we adapt from DANE, to handle federated learning.
  arXiv  Detail & Related papers  (2020-01-07T07:44:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.